Özet:

Remote sensing technologies are used in many applications to extract information from the surface of the earth. Image classification, which is one of the most widely-used ways of information extraction, is a controversial topic in remote sensing. This is because all classification algorithms introduced in the literature cause classification errors to some extent. Simple classification algorithms like Minimum Distance, Parallelpiped and Mahalanobis Distance commit a large amount of classification errors. This, of course, has encouraged the remote sensing community to develop more advanced classification algorithms to further increase classification accuracy. This study uses sophisticated classification algorithms Support Vector Machines (SVM), k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN) to classify a WorldView-2 multispectral image in order to produce land cover maps. The accuracies of the produced thematic maps were evaluated with randomly-selected control points. The SVM algorithm classified the imagery with the best classification accuracy of 72.38%.

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